dasymetric

Dasymetric mapping is a family of areal interpolation techniques that improve the spatial allocation of aggregated data by using ancillary information to partition and redistribute values within source zones. Unlike simple proportional allocation that assumes even distribution, dasymetric methods adjust density according to features likely to influence the distribution, such as land use, building footprints, or infrastructure.

Typically the input data are areal counts for a set of units (for example, population by census tracts). Ancillary data provide a mask or attribute that helps distinguish areas within those units that are more or less likely to contain the phenomenon of interest. The goal is to produce a finer-resolution representation that preserves the total value within each source polygon while reflecting the spatial pattern suggested by the ancillary information.

Methodology involves subdividing each source polygon into subareas defined by the ancillary data. A density or weight is assigned to each subarea, and the total count is redistributed so that the polygon’s original value is maintained but distributed according to the subarea proportions. Variants include binary dasymetric mapping, which uses a mask to include only certain subareas, and density-based methods, which allow varying densities across subareas. Automated approaches integrate remote sensing, land cover classification, or urban footprint data to inform the redistribution.

Applications span population distribution, housing density, service provision planning, public health, transportation modeling, and environmental exposure assessment. Limitations include dependence on the quality and resolution of ancillary data, potential subjectivity in mask or class definitions, and sensitivity to how subareas are delineated. Despite challenges, dasymetric mapping offers a more realistic representation of spatial phenomena than traditional areal interpolation in many contexts.